This repository contains my lecture notes and solved problem sets from Stanford's EE261: The Fourier Transform and its Applications, focusing on foundational concepts in Fourier analysis. These topics are crucial for Signal Processing, Control Systems, and Robotics.

1. Fourier Series: Representation of periodic signals using trigonometric functions.
2. The Fourier Transform: Continuous and discrete transforms, properties, and applications.
3. Dirac Delta and Generalized Functions: Understanding distributions for real-world problems.
4. Convolution and Correlation: Signal filtering, probability distributions, and linear systems.
5. Sampling Theory: Nyquist-Shannon theorem, reconstruction, and discrete signals.
6. Discrete Fourier Transform (DFT) and FFT: Fast algorithms for numerical computation.
7. Multidimensional Fourier Transform: Applications in imaging and optics.

• Lecture Notes: Summarized theoretical concepts from lectures.
• Problem Sets: Solutions to exercises for deeper understanding and application.

• Official Course Website: Stanford EE261
• Lecture Videos: YouTube Playlist

I am focused on building a strong theoretical foundational math skill, which will be crucial for my future work in Robotics research.

Feel free to reach out:

• Email: sampath@umich.edu
• LinkedIn: linkedin.com/in/sai-sampath-kedari